Self-supervised/supervised depth completion. We now show how filtering outliers improves the performance of networks for depth completion, the most iconic task performed starting from LiDAR depth maps, without specifically retraining neither our framework nor the depth completion network. Following [1]}, Table REF  shows results achieved by the Sparse-to-Dense framework – using the weights released by the authors trained either without (a) [2]} or with (b) [3]} supervision – when processing inputs that have been filtered through unsupervised techniques like ours and Surface [1]}. We compute standard depth completion metrics, such as RMSE and Mean Absolute Error (MAE), inverse RMSE and inverse MAE on points up to 50m, to focus on the foreground objects (mostly affected by the outliers). Concerning the self-supervised variant (a), we can notice how filtering with Surface [1]} improves all metrics by removing nearly 4% of the total pixels with available LiDAR values. Concerning our method, we can achieve a larger improvement by limiting this percentage to 1.20%, hinting that more precise filtering of the outliers, yet limited to fewer pixels, is more effective for the depth completion task. This is confirmed by experiments on the supervised variant (b): in this case, using Surface [1]} only improves inverse metrics, while our method always improves all metrics by removing 0.70% pixels only, resulting slightly worse only in iMAE compared to Surface.
Finally, we report (c) experiments with PENet [7]}, a state-of-the-art framework for supervised completion, which confirm the previous findings.
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